In this application, we request continuation of MH57881, "Genetic Association in Schizophrenia and Other Disorders". In our previous aims, we targeted the development of statistical methods for fine-mapping and mapping variants affecting liability to simple and complex disease. Much of the proposed work focused on haplotypes. Our emphasis was apparently timely given the current interest in haplotypes and their use for finding genetic variants affecting liability to disease. During the next five years, we propose to continue some of the methodological work on haplotypes, especially in three areas: (1) association analysis guided by the evolutionary relationships among haplotypes; (2) methods to cluster haplotypes before testing hypotheses, which serves to reduce the dimension of the test and potentially increase its power; and, (3) complementing the HapMap project, develop statistical methods to define the multilocus structure of linkage disequilibrium within regions of the human genome, as well as developing related statistics. Two new thrusts include approaches to analyze large, complex models, such as those that arise when genetic variants in different genes affect liability (liability alleles), potentially through their interaction and the use of admixture mapping to find liability alleles. Regarding complex models, we plan to extend False Discovery Rate (FDR) methods to the setting of multiple, dependent variables and multistage FDR. Multistage FDR will be evaluated as a tool for finding gene-gene interactions in which "main effects" of the genes are also detectable. To find gene-gene interactions without detectable main effects, we propose refinements of model selection. For admixture mapping, we take the view that the properties of the likelihood are incompletely understood. Therefore we propose to study its properties. We also propose refinement to models for admixture mapping that account for uncertainty in ancestral allele distributions and dependent markers.